Simplify app using gr.Interface and use HF default gradio

app.py

@@ -28,7 +28,7 @@ def generate_fft(image):
 def predict(image):
     """Main prediction function."""
     if image is None:
-        return None, "Please upload an image", None
+        return "Please upload an image", None
     
     try:
         img = image.convert('RGB').resize((128, 128))
@@ -44,8 +44,7 @@ def predict(image):
             label = "REAL (Authentic)"
             confidence = (1 - prediction) * 100
         
-        result_text = f"""
-## Detection Result: {label}
+        result_text = f"""## Detection Result: {label}
 
 **Confidence:** {confidence:.2f}%
 
@@ -68,68 +67,34 @@ This model is trained on StyleGAN-generated faces and may not accurately detect
         return f"Error: {str(e)}", None
 
 
-with gr.Blocks(
+# Simple Interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Face Image"),
+    outputs=[
+        gr.Markdown(label="Detection Result"),
+        gr.Image(label="FFT Frequency Analysis")
+    ],
     title="DeepGuard - AI Face Authenticator",
-    theme=gr.themes.Base(primary_hue="green", secondary_hue="blue", neutral_hue="gray")
-) as demo:
-    
-    gr.HTML("""
-    <div style="text-align: center; margin-bottom: 1rem;">
-        <h1 style="color: #00f260; font-size: 2.5rem;">DeepGuard</h1>
-        <p style="color: #888;">AI Face Authenticator - Deepfake Detection</p>
-    </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            input_image = gr.Image(label="Upload Image", type="pil", height=300)
-            analyze_btn = gr.Button("Analyze Image", variant="primary", size="lg")
-    
-    with gr.Row():
-        with gr.Column(scale=2):
-            result_output = gr.Markdown(label="Detection Result")
-        with gr.Column(scale=1):
-            fft_output = gr.Image(label="FFT Frequency Analysis", height=200)
-    
-    with gr.Accordion("How It Works", open=False):
-        gr.Markdown("""
-### XceptionTransfer Deep Learning Model
-
-- **Architecture:** Transfer learning with Xception backbone
+    description="Detect AI-generated (deepfake) faces using deep learning. Upload a face image for analysis.",
+    article="""
+### Model Info
+- **Architecture:** XceptionTransfer
 - **Training Data:** 140,000 real and GAN-generated faces
 - **Accuracy:** 88% on test dataset
-- **ROC-AUC:** 95%
-        """)
-    
-    with gr.Accordion("FFT Interpretation Guide", open=False):
-        gr.Markdown("""
-| Pattern | Interpretation |
-|---------|----------------|
-| Bright center spot | Normal low-frequency content |
-| Radiating spokes | Edge directions in original image |
-| Random noise distribution | Natural texture (real photos) |
-| Grid or cross artifacts | Potential GAN fingerprint |
-
-*GAN artifacts in FFT are subtle and require trained interpretation.*
-        """)
-    
-    with gr.Accordion("Model Limitations", open=False):
-        gr.Markdown("""
-This model is trained on StyleGAN-generated faces only.
 
-It may NOT accurately detect:
-- Stable Diffusion / Midjourney / DALL-E images
-- Non-face subjects
-- Heavily edited images
-        """)
-    
-    gr.HTML("""<div style="text-align: center; margin-top: 2rem; color: #666;">
-        Built with TensorFlow and MLflow | DeepGuard MLOps Pipeline
-    </div>""")
-    
-    analyze_btn.click(fn=predict, inputs=input_image, outputs=[result_output, fft_output])
-    input_image.change(fn=predict, inputs=input_image, outputs=[result_output, fft_output])
+### FFT Interpretation
+| Pattern | Meaning |
+|---------|---------|
+| Bright center | Normal low-frequency content |
+| Radiating spokes | Edge directions in image |
+| Grid artifacts | Potential GAN fingerprint |
 
+### Limitations
+This model is trained on StyleGAN faces only. It may not detect Stable Diffusion, Midjourney, or DALL-E images.
+""",
+    allow_flagging="never"
+)
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()

requirements.txt

@@ -1,5 +1,3 @@
-gradio==4.19.2
-huggingface_hub==0.20.3
 tensorflow==2.17.0
 numpy>=1.24.0
 Pillow>=10.0.0